activity
20192021
most citedPredicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Information

2 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2021

Predicting the Factuality of Reporting of News Media Using Observations About User Attention in Their YouTube Channels

Krasimira Bozhanova, Yoan Dinkov, Ivan Koychev +3

We propose a novel framework for predicting the factuality of reporting of news media outlets by studying the user attention cycles in their YouTube channels. In particular, we des…

cs.CL2020

EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering

Momchil Hardalov, Todor Mihaylov, Dimitrina Zlatkova +3

We propose EXAMS -- a new benchmark dataset for cross-lingual and multilingual question answering for high school examinations. We collected more than 24,000 high-quality high scho…

cs.CL2020

What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context

Ramy Baly, Georgi Karadzhov, Jisun An +5

Predicting the political bias and the factuality of reporting of entire news outlets are critical elements of media profiling, which is an understudied but an increasingly importan…

cs.CL20192 cited

Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Information

Yoan Dinkov, Ahmed Ali, Ivan Koychev +1

We address the problem of predicting the leading political ideology, i.e., left-center-right bias, for YouTube channels of news media. Previous work on the problem has focused excl…

cs.CL2019

Detecting Toxicity in News Articles: Application to Bulgarian

Yoan Dinkov, Ivan Koychev, Preslav Nakov

Online media aim for reaching ever bigger audience and for attracting ever longer attention span. This competition creates an environment that rewards sensational, fake, and toxic…